OpenRaiser/Intern-Atlas huggingface dataset:
Layered SGD: A Decentralized and Synchronous SGD Algorithm for Scalable Deep Neural Network Training
Venue: arXiv cs.LG 2019
Year: 2019

Stochastic Gradient Descent (SGD) is the most popular algorithm for training deep neural networks (DNNs). As larger networks and datasets cause longer training times, training on distributed systems is common and distributed SGD variants, mainly asynchronous and synchronous SGD, are widely used. Asynchronous SGD is communication efficient but suffers from accuracy degradation due to delayed parameter updating. Synchronous SGD becomes communication intensive when the number of nodes increases regardless of its advantage. To address these issues, we introduce Layered SGD (LSGD), a new decentralized synchronous SGD algorithm. LSGD partitions computing resources into subgroups that each contain a communication layer (communicator) and a computation layer (worker). Each subgroup has centralized communication for parameter updates while communication between subgroups is handled by communicators. As a result, communication time is overlapped with I/O latency of workers. The efficiency of the algorithm is tested by training a deep network on the ImageNet classification task.
Scalable Knowledge Graph Construction from Twitter
Venue: arXiv cs.IR 2019
Year: 2019

We describe a knowledge graph derived from Twitter data with the goal of discovering relationships between people, links, and topics. The goal is to filter out noise from Twitter and surface an inside-out view that relies on high quality content. The generated graph contains many relationships where the user can query and traverse the structure from different angles allowing the development of new applications.
U2Fi: A Provisioning Scheme of IoT Devices with Universal Cryptographic Tokens
Venue: arXiv cs.CR 2019
Year: 2019

Provisioning is the starting point of the whole life-cycle of IoT devices. The traditional provisioning methods of IoT devices are facing several issues, either about user experience or privacy harvesting. Moreover, IoT devices are vulnerable to different levels of attacks due to limited resources and long online duration. In this paper, we proposed U2Fi, a novel provisioning scheme for IoT devices. We provide a solution to make the U2F device that has been trusted by the cloud in the distribution process, via WiFi or its side channel, to provision the new IoT device. Further, subsequent device settings modification, setting update, and owner transfer can also be performed by using a U2F device that has been trusted to improve security and provide a better user experience. This could provide helpful user friendliness to some valuable new application scenarios in IoT, such as smart hotel. Users could migrate the whole authentication of smart devices into a new site by simply inserting the universal cryptographic token into the secure gateway and authorizing by pressing the user-presence button on the token. Besides, the relevant unbinding process could also be done with a single cryptographic operation signed by the cryptographic token.
Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders
Venue: arXiv cs.CV 2019
Year: 2019

This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encoders (VAEs) with a shared latent space. The VAEs are trained to generate online and offline handwritten Latin characters simultaneously. In this way, we create a cross-modal VAE (Cross-VAE). During training, the proposed Cross-VAE is trained to minimize the reconstruction loss of the two modalities, the distribution loss of the two VAEs, and a novel third loss called the space sharing loss. This third, space sharing loss is used to encourage the modalities to share the same latent space by calculating the distance between the latent variables. Through the proposed method mutual conversion of online and offline handwritten characters is possible. In this paper, we demonstrate the performance of the Cross-VAE through qualitative and quantitative analysis.
Gathering with extremely restricted visibility
Venue: arXiv cs.DC 2019
Year: 2019

We consider the classical problem of making mobile processes gather or converge at a same position (as performed by swarms of animals in Nature). Existing works assume that each process can see all other processes, or all processes within a certain radius. In this paper, we introduce a new model with an extremely restricted visibility: each process can only see one other process (its closest neighbor). Our goal is to see if (and to what extent) the gathering and convergence problems can be solved in this setting. We first show that, surprisingly, the problem can be solved for a small number of processes (at most 5), but not beyond. This is due to indeterminacy in the case where there are several closest neighbors for a same process. By removing this indeterminacy with an additional hypothesis (choosing the closest neighbor according to an order on the positions of processes), we then show that the problem can be solved for any number of processes. We also show that up to one crash failure can be tolerated for the convergence problem.
An analysis of community structure in Brazilian political topic-based Twitter networks
Venue: arXiv cs.SI 2019
Year: 2019

Online social networks such as Twitter are important platforms for spreading public opinion on a variety of subjects. The classification of users through the analysis of their posts on Twitter according to their opinion sharing can help marketing ads and political campaigns to focus on specific user groups. Community detection-based techniques are specially useful to classify Twitter users, as they do not require rule-based methods or labeled data to perform the clustering task. In this paper, we constructed networks using data related to political discussions in Brazil extracted from Twitter. We show that (i) these networks follow the power-law distribution, indicating that a few popular users are responsible for most of the "mentions" and "retweets"; (ii) the most popular tweets are viral and spread across the communities whereas most of the remaining tweets are trapped in the communities where they originated; and (iii) words associated with positive sentiments are predominant in network communities related to the Brazilian presidential elections and appear in viral tweets.
Realistic Speech-Driven Facial Animation with GANs
Venue: arXiv cs.CV 2019
Year: 2019

Speech-driven facial animation is the process that automatically synthesizes talking characters based on speech signals. The majority of work in this domain creates a mapping from audio features to visual features. This approach often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present an end-to-end system that generates videos of a talking head, using only a still image of a person and an audio clip containing speech, without relying on handcrafted intermediate features. Our method generates videos which have (a) lip movements that are in sync with the audio and (b) natural facial expressions such as blinks and eyebrow movements. Our temporal GAN uses 3 discriminators focused on achieving detailed frames, audio-visual synchronization, and realistic expressions. We quantify the contribution of each component in our model using an ablation study and we provide insights into the latent representation of the model. The generated videos are evaluated based on sharpness, reconstruction quality, lip-reading accuracy, synchronization as well as their ability to generate natural blinks.
A Blockchain Policy and Charging Control Framework for Roaming in Cellular Networks
Venue: arXiv cs.NI 2019
Year: 2019

As a technology foundation of cryptocurrencies, blockchain enables decentralized peer-to-peer trading through consensus mechanisms without the involvement of a third party. Blockchain has been regarded as an auspicious technology for future cellular networks. It is able to provide solutions to problems related to mobile operators and user trust, embedded smart contracts, security concerns, pricing (e.g. for roaming), etc. When applying blockchain to cellular networks, there are significant challenges in terms of deployment and application, due to resource-constrained transactions. This article begins by introducing the basic concept of blockchain and then moves on to illustrate its benefits and limitations in the roaming system. Two models of roaming-based blockchain technologies are offered to show their suitability for cellular networks as opposed to traditional technology. Finally, potential issues and challenges of roaming-based blockchains are addressed and evaluated using the roaming use case in the EU.
Data-Driven Machine Learning Techniques for Self-healing in Cellular Wireless Networks: Challenges and Solutions
Venue: arXiv cs.NI 2019
Year: 2019

For enabling automatic deployment and management of cellular networks, the concept of self-organizing network (SON) was introduced. SON capabilities can enhance network performance, improve service quality, and reduce operational and capital expenditure (OPEX/CAPEX). As an important component in SON, self-healing is defined as a network paradigm where the faults of target networks are mitigated or recovered by automatically triggering a series of actions such as detection, diagnosis and compensation. Data-driven machine learning has been recognized as a powerful tool to bring intelligence into network and to realize self-healing. However, there are major challenges for practical applications of machine learning techniques for self-healing. In this article, we first classify these challenges into five categories: 1) data imbalance, 2) data insufficiency, 3) cost insensitivity, 4) non-real-time response, and 5) multi-source data fusion. Then we provide potential technical solutions to address these challenges. Furthermore, a case study of cost-sensitive fault detection with imbalanced data is provided to illustrate the feasibility and effectiveness of the suggested solutions.
Supracentrality Analysis of Temporal Networks with Directed Interlayer Coupling
Venue: arXiv cs.SI 2019
Year: 2019

We describe centralities in temporal networks using a supracentrality framework to study centrality trajectories, which characterize how the importances of nodes change in time. We study supracentrality generalizations of eigenvector-based centralities, a family of centrality measures for time-independent networks that includes PageRank, hub and authority scores, and eigenvector centrality. We start with a sequence of adjacency matrices, each of which represents a time layer of a network at a different point or interval of time. Coupling centrality matrices across time layers with weighted interlayer edges yields a \emph{supracentrality matrix} $\mathbb{C}(ω)$, where $ω$ controls the extent to which centrality trajectories change over time. We can flexibly tune the weight and topology of the interlayer coupling to cater to different scientific applications. The entries of the dominant eigenvector of $\mathbb{C}(ω)$ represent \emph{joint centralities}, which simultaneously quantify the importance of every node in every time layer. Inspired by probability theory, we also compute \emph{marginal} and \emph{conditional centralities}. We illustrate how to adjust the coupling between time layers to tune the extent to which nodes' centrality trajectories are influenced by the oldest and newest time layers. We support our findings by analysis in the limits of small and large $ω$.
Instance Segmentation with Point Supervision
Venue: arXiv cs.CV 2019
Year: 2019

Instance segmentation methods often require costly per-pixel labels. We propose a method that only requires point-level annotations. During training, the model only has access to a single pixel label per object, yet the task is to output full segmentation masks. To address this challenge, we construct a network with two branches: (1) a localization network (L-Net) that predicts the location of each object; and (2) an embedding network (E-Net) that learns an embedding space where pixels of the same object are close. The segmentation masks for the located objects are obtained by grouping pixels with similar embeddings. At training time, while L-Net only requires point-level annotations, E-Net uses pseudo-labels generated by a class-agnostic object proposal method. We evaluate our approach on PASCAL VOC, COCO, KITTI and CityScapes datasets. The experiments show that our method (1) obtains competitive results compared to fully-supervised methods in certain scenarios; (2) outperforms fully- and weakly- supervised methods with a fixed annotation budget; and (3) is a first strong baseline for instance segmentation with point-level supervision.
"My Way of Telling a Story": Persona based Grounded Story Generation
Venue: arXiv cs.CL 2019
Year: 2019

Visual storytelling is the task of generating stories based on a sequence of images. Inspired by the recent works in neural generation focusing on controlling the form of text, this paper explores the idea of generating these stories in different personas. However, one of the main challenges of performing this task is the lack of a dataset of visual stories in different personas. Having said that, there are independent datasets for both visual storytelling and annotated sentences for various persona. In this paper we describe an approach to overcome this by getting labelled persona data from a different task and leveraging those annotations to perform persona based story generation. We inspect various ways of incorporating personality in both the encoder and the decoder representations to steer the generation in the target direction. To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand. In our experiments we use five different personas to guide the generation process. We find that the models based on our hypotheses perform better at capturing words while generating stories in the target persona.
A Strategy for Expert Recommendation From Open Data Available on the Lattes Platform
Venue: arXiv cs.IR 2019
Year: 2019

With the increasing volume of data and users of curriculum systems, the difficulty of finding specialists is increasing.This work proposes an open data extraction methodology of the Lattes Platform curricula, a treatment for this data and investigates a Recommendation Agent approach based on deep neural networks with autoencoder.
Action-Sensitive Phonological Dependencies
Venue: arXiv cs.CL 2019
Year: 2019

This paper defines a subregular class of functions called the tier-based synchronized strictly local (TSSL) functions. These functions are similar to the the tier-based input-output strictly local (TIOSL) functions, except that the locality condition is enforced not on the input and output streams, but on the computation history of the minimal subsequential finite-state transducer. We show that TSSL functions naturally describe rhythmic syncope while TIOSL functions cannot, and we argue that TSSL functions provide a more restricted characterization of rhythmic syncope than existing treatments within Optimality Theory.
RECAL: Reuse of Established CNN classifer Apropos unsupervised Learning paradigm
Venue: arXiv cs.CV 2019
Year: 2019

Recently, clustering with deep network framework has attracted attention of several researchers in the computer vision community. Deep framework gains extensive attention due to its efficiency and scalability towards large-scale and high-dimensional data. In this paper, we transform supervised CNN classifier architecture into an unsupervised clustering model, called RECAL, which jointly learns discriminative embedding subspace and cluster labels. RECAL is made up of feature extraction layers which are convolutional, followed by unsupervised classifier layers which is fully connected. A multinomial logistic regression function (softmax) stacked on top of classifier layers. We train this network using stochastic gradient descent (SGD) optimizer. However, the successful implementation of our model is revolved around the design of loss function. Our loss function uses the heuristics that true partitioning entails lower entropy given that the class distribution is not heavily skewed. This is a trade-off between the situations of "skewed distribution" and "low-entropy". To handle this, we have proposed classification entropy and class entropy which are the two components of our loss function. In this approach, size of the mini-batch should be kept high. Experimental results indicate the consistent and competitive behavior of our model for clustering well-known digit, multi-viewed object and face datasets. Morever, we use this model to generate unsupervised patch segmentation for multi-spectral LISS-IV images. We observe that it is able to distinguish built-up area, wet land, vegetation and waterbody from the underlying scene.
A Hierarchical Attention Based Seq2seq Model for Chinese Lyrics Generation
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we comprehensively study on context-aware generation of Chinese song lyrics. Conventional text generative models generate a sequence or sentence word by word, failing to consider the contextual relationship between sentences. Taking account into the characteristics of lyrics, a hierarchical attention based Seq2Seq (Sequence-to-Sequence) model is proposed for Chinese lyrics generation. With encoding of word-level and sentence-level contextual information, this model promotes the topic relevance and consistency of generation. A large Chinese lyrics corpus is also leveraged for model training. Eventually, results of automatic and human evaluations demonstrate that our model is able to compose complete Chinese lyrics with one united topic constraint.
Mixed projection- and density-based topology optimization with applications to structural assemblies
Venue: arXiv cs.CE 2019
Year: 2019

In this paper we present a mixed projection- and density-based topology optimization approach. The aim is to combine the benefits of both parametrizations: the explicit geometric representation provides specific controls on certain design regions while the implicit density representation provides the ultimate design freedom elsewhere. This approach is particularly suited for structural assemblies, where the optimization of the structural topology is coupled with the optimization of the shape of the interface between the sub-components in a unified formulation. The interface between the assemblies is defined by a segmented profile made of linear geometric entities. The geometric coordinates of the nodes connecting the profile segments are used as shape variables in the problem, together with density variables as in conventional topology optimization. The variable profile is used to locally impose specific geometric constraints or to project particular material properties. Examples of the properties considered herein are a local volume constraint, a local maximum length scale control, a variable Young's modulus for the distributed solid material, and spatially variable minimum and maximum length scale. The resulting optimization approach is general and various geometric entities can be used. The potential for complex design manipulations is demonstrated through several numerical examples.
Exploiting Physical Contacts for Robustness Improvement of a Dot-Painting Mission by a Micro Air Vehicle
Venue: arXiv cs.RO 2019
Year: 2019

In this paper we address the problem of dot painting on a wall by a quadrotor Micro Air Vehicle (MAV), using on-board low cost sensors (monocular camera and IMU) for localization. A method is proposed to cope with uncertainties on the initial positioning of the MAV with respect to the wall and to deal with walls composed of multiple segments. This method is based on an online estimation algorithm that makes use of information of physical contacts detected by the drone during the flight to improve the positioning accuracy of the painted dots. Simulation results are presented to assess quantitatively the efficiency of the proposed approaches.
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

As the development of deep neural networks, 3D object recognition is becoming increasingly popular in computer vision community. Many multi-view based methods are proposed to improve the category recognition accuracy. These approaches mainly rely on multi-view images which are rendered with the whole circumference. In real-world applications, however, 3D objects are mostly observed from partial viewpoints in a less range. Therefore, we propose a multi-view based 3D convolutional neural network, which takes only part of contiguous multi-view images as input and can still maintain high accuracy. Moreover, our model takes these view images as a joint variable to better learn spatially correlated features using 3D convolution and 3D max-pooling layers. Experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images which are captured from partial angles with less range. The results on 3D rotated real image dataset MIRO further demonstrate that MV-C3D is more adaptable in real-world scenarios. The classification accuracy can be further improved with the increasing number of view images.
Modeling Consonance and its Relationships with Temperament, Harmony, and Electronic Amplification
Venue: arXiv cs.SD 2019
Year: 2019

After briefly revising the concepts of consonance/dissonance, a respective mathematic-computational model is described, based on Helmholtz's consonance theory and also considering the partials intensity. It is then applied to characterize five scale temperaments, as well as some minor and major triads and electronic amplification. In spite of the simplicity of the described model, a surprising agreement is often observed between the obtained consonances/dissonances and the typically observed properties of scales and chords. The representation of temperaments as graphs where links correspond to consonance (or dissonance) is presented and used to compare distinct temperaments, allowing the identification of two main groups of scales. The interesting issue of nonlinearities in electronic music amplification is also addressed while considering quadratic distortions, and it is shown that such nonlinearities can have drastic effect in changing the original patterns of consonance and dissonance.
LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding
Venue: arXiv cs.LG 2019
Year: 2019

Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning. A sufficient number of researchers have implemented ways to explain indifferently any black box model for classification tasks. A drawback of building agnostic explanators is that the neighbourhood generation process is universal and consequently does not guarantee true adjacency between the generated neighbours and the instance. This paper explores a methodology on providing explanations for a neural network's decisions, in a local scope, through a process that actively takes into consideration the neural network's architecture on creating an instance's neighbourhood, that assures the adjacency among the generated neighbours and the instance.